Scatter correction based on time-of-flight measurements for radiography and photon counting CT
Bibliographic record
Abstract
Time-of-flight (ToF) CT and radiography are explored as a novel method for scatter estimation and correction for state-of-the-art medical X-ray imaging with increased sensitivity. By measuring the ToF of X photons from the source to the detector, the scattered contribution can be estimated in each pixel individually, owing to the longer paths taken by scattered X photons. While an entire scan can be made this way, there is some concern about the duration of the acquisition since measuring ToF requires a pulsed X-ray source with a very low duty cycle (1-5%). As an alternative, this paper explores the use of ToF-CT to correct for scatter contribution in a regular photon counting projection image. It is validated at different timing resolutions and for different ToF scan durations using Monte Carlo simulations performed with GATE. The results show that with a 10 ps FWHM timing resolution, the method is capable of accurately estimating and correcting for $98 \%$ of the scatter while keeping a sensitivity to primary photons over $98 \%$ in photon counting mode. Between 50 and 150 ps, the scatter correction performance is comparable to an anti-scatter grid’s, but without any loss in sensitivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".